Leveraging in-network real-value computation for home network device recognition

Matthews Jose (1, 2) , Kahina Lazri (1) , Jérôme François (2) , Olivier Festor (3, 2)

1 Orange Labs [Chatillon]
2 RESIST - Resilience and Elasticity for Security and ScalabiliTy of dynamic networked systems
3 TELECOM Nancy

Current generation of switches are highly programmable and able to support stateful. However, these switches cannot perform floating point operations. As a result several network applications have to be run on external servers or middleboxes in the network. We introduce InREC, a system that extends the capabilities of programmable switches to support in-network real-valued operations using the IEEE half-precision floating point representation. Our demo on a Barefoot Tofino switches demonstrates the efficiency of InREC for in-network computation by computing a logistic regression function to classify devices (IoT and laptop) in a simulated home environment.

Domaines: Réseaux et télécommunications [cs.NI]

Matthews Jose, Kahina Lazri, Jérôme François, Olivier Festor. Leveraging in-network real-value computation for home network device recognition. IM 2021 - IFIP/IEEE International Symposium on Integrated Network Management (Demo), May 2021, Bordeaux / Virtual, France. ⟨hal-03525070⟩